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Related Concept Videos

Diffusion01:21

Diffusion

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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Related Experiment Video

Updated: Jan 8, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Composite material surface microscopic defect detection and classification combining diffusion models and zero-shot

Weijun Fan1

  • 1School of Science, Jimei University, Xiamen, 361021, China. cixt2873@outlook.com.

Scientific Reports
|December 19, 2025
PubMed
Summary

This study introduces a novel framework for detecting microscopic defects in composite materials, overcoming data scarcity and identifying unknown defect types. The approach combines diffusion models and zero-shot learning for efficient, intelligent quality control.

Keywords:
Composite material defect detectionDiffusion modelIntelligent manufacturingVision-semantic mappingZero-shot learning

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Area of Science:

  • Materials Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Microscopic defect detection in composite materials faces challenges with limited data and identifying novel defect types.
  • Existing methods often require extensive labeled datasets, increasing costs and limiting adaptability.

Purpose of the Study:

  • To develop an innovative framework for microscopic defect detection in composite materials.
  • To address data scarcity and enable the identification of unseen defect types.
  • To enhance intelligent quality control in composite materials manufacturing.

Main Methods:

  • A dual-path collaborative architecture integrating a diffusion model and zero-shot learning.
  • Conditional diffusion process to learn defect distribution characteristics.
  • Vision-semantic joint embedding space for cross-modal knowledge transfer.

Main Results:

  • The framework achieved detection accuracy comparable to supervised learning without labeled samples.
  • Significantly reduced labeling costs and demonstrated excellent generalization across different composite materials.
  • Verified effectiveness on multiple benchmark datasets.

Conclusions:

  • The proposed framework offers an efficient technical solution for intelligent quality control in composite materials manufacturing.
  • It advances industrial defect detection technology towards greater intelligence and adaptability.
  • The integration of diffusion models and zero-shot learning proves effective for data-scarce defect detection.